Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming
Created by W.Langdon from
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- @Article{Hu:2012:GPEM,
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author = "Ting Hu and Joshua Payne and Wolfgang Banzhaf and
Jason Moore",
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title = "Evolutionary dynamics on multiple scales: a
quantitative analysis of the interplay between
genotype, phenotype, and fitness in linear genetic
programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2012",
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volume = "13",
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number = "3",
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pages = "305--337",
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month = sep,
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note = "Special issue on selected papers from the 2011
European conference on genetic programming",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
Accessibility, Coreness, Evolvability,
Genotype-phenotype map, Phenotype-fitness map,
Networks, Neutrality, Redundancy, Robustness",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-012-9159-4",
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size = "33 pages",
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abstract = "Redundancy is a ubiquitous feature of genetic
programming (GP), with many-to-one mappings commonly
observed between genotype and phenotype, and between
phenotype and fitness. If a representation is
redundant, then neutral mutations are possible. A
mutation is phenotypically-neutral if its application
to a genotype does not lead to a change in phenotype. A
mutation is fitness-neutral if its application to a
genotype does not lead to a change in fitness. Whether
such neutrality has any benefit for GP remains a
contentious topic, with reported experimental results
supporting both sides of the debate. Most existing
studies use performance statistics, such as success
rate or search efficiency, to investigate the utility
of neutrality in GP. Here, we take a different tack and
use a measure of robustness to quantify the neutrality
associated with each genotype, phenotype, and fitness
value. We argue that understanding the influence of
neutrality on GP requires an understanding of the
distributions of robustness at these three levels, and
of the interplay between robustness, evolvability, and
accessibility amongst genotypes, phenotypes, and
fitness values. As a concrete example, we consider a
simple linear genetic programming system that is
amenable to exhaustive enumeration and allows for the
full characterisation of these quantities, which we
then relate to the dynamical properties of simple
mutation-based evolutionary processes. Our results
demonstrate that it is not only the distribution of
robustness amongst phenotypes that affects evolutionary
search, but also (1) the distributions of robustness at
the genotypic and fitness levels and (2) the mutational
biases that exist amongst genotypes, phenotypes, and
fitness values. Of crucial importance is the
relationship between the robustness of a genotype and
its mutational bias toward other phenotypes.",
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notes = "EuroGP 2011 \cite{Silva:2011:GP}",
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affiliation = "Computational Genetics Laboratory, Dartmouth Medical
School, Hanover, NH, USA",
- }
Genetic Programming entries for
Ting Hu
Joshua L Payne
Wolfgang Banzhaf
Jason H Moore
Citations